-The conference starts on September 3rd evening by an informal meeting.


-Plenary Speakers:


Jose C. Principe

Title: TBA
Abstract: TBA

Roman Belavkin

Title: Towards a Dynamic Value of Information Theory

The value of information (VoI) theory was developed in the 1960s by Ruslan Stratonovich
and colleagues. Inspired by Shannon’s rate distortion theory, it defines VoI as the maximum expected utility (or the minimum expected cost) that can be achieved subject to a given information constraint. Different value functions correspond to different types of information and different optimal Markov transition probabilities. In many natural systems, such as learning and evolving systems, the information amount itself is dynamic, and here we discuss dynamical extension of the value of information theory. We formulate the corresponding variational problems defining certain geodesic curves on statistical manifolds and discuss the resulting theory. Examples for Shannon’s information and certain types of utility functions will be used for illustration. The problem of optimal control of mutation rates in  evolutionary systems will be considered as an application of the theory.

Panos Pardalos 

Title: TBA
Abstract: TBA

Martin Schmid

Title: Search in Imperfect Information Games
From the very dawn of the field, se
arch with value functions was a fundamental concept of computer games research. Turing’s chess algorithm from 1950 was able to think two moves ahead, and Shannon’s work on chess from 1950 includes an extensive section on evaluation functions to be used within a search. Samuel’s checkers program from 1959 already combines search and value functions that are learned through selfplay and bootstrapping. TDGammon improves upon those ideas and uses neural networks to learn those complex value functions only to be again used within search. The combination of decisiontime search and value functions has been present in the remarkable milestones where computers bested their human counterparts in long standing challenging gamesDeepBlue for Chess and AlphaGo for Go. Until recently, this powerful framework of search aided with (learned) value functions has been limited to perfect information games. We will talk about why search matters, and about generalizing search for imperfect information games.